Overview

Dataset statistics

Number of variables22
Number of observations91
Missing cells783
Missing cells (%)39.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.4 KiB
Average record size in memory184.0 B

Variable types

Text2
Unsupported3
Categorical4
Numeric13

Alerts

Region has constant value ""Constant
Division has constant value ""Constant
Games_Level has constant value ""Constant
Qualifier has constant value ""Constant
Back Squat (lbs) is highly overall correlated with Clean and Jerk (lbs) and 2 other fieldsHigh correlation
Clean and Jerk (lbs) is highly overall correlated with Back Squat (lbs) and 2 other fieldsHigh correlation
Deadlift (lbs) is highly overall correlated with Back Squat (lbs) and 2 other fieldsHigh correlation
Fight Gone Bad is highly overall correlated with Filthy 50 (s) and 3 other fieldsHigh correlation
Filthy 50 (s) is highly overall correlated with Clean and Jerk (lbs) and 4 other fieldsHigh correlation
Fran (s) is highly overall correlated with Grace (s)High correlation
Grace (s) is highly overall correlated with Fight Gone Bad and 1 other fieldsHigh correlation
Helen (s) is highly overall correlated with Deadlift (lbs) and 1 other fieldsHigh correlation
Run 5k (s) is highly overall correlated with Fight Gone Bad and 1 other fieldsHigh correlation
Snatch (lbs) is highly overall correlated with Back Squat (lbs) and 3 other fieldsHigh correlation
Sprint 400m (s) is highly overall correlated with Deadlift (lbs) and 2 other fieldsHigh correlation
Affiliate has 15 (16.5%) missing valuesMissing
Country has 91 (100.0%) missing valuesMissing
Back Squat (lbs) has 6 (6.6%) missing valuesMissing
Clean and Jerk (lbs) has 6 (6.6%) missing valuesMissing
Deadlift (lbs) has 8 (8.8%) missing valuesMissing
Snatch (lbs) has 6 (6.6%) missing valuesMissing
Fight Gone Bad has 72 (79.1%) missing valuesMissing
Max Pull-ups has 45 (49.5%) missing valuesMissing
Chad1000x (s) has 91 (100.0%) missing valuesMissing
L1 Benchmark (s) has 91 (100.0%) missing valuesMissing
Filthy 50 (s) has 83 (91.2%) missing valuesMissing
Fran (s) has 26 (28.6%) missing valuesMissing
Grace (s) has 48 (52.7%) missing valuesMissing
Helen (s) has 72 (79.1%) missing valuesMissing
Run 5k (s) has 55 (60.4%) missing valuesMissing
Sprint 400m (s) has 68 (74.7%) missing valuesMissing
Athlete has unique valuesUnique
Country is an unsupported type, check if it needs cleaning or further analysisUnsupported
Chad1000x (s) is an unsupported type, check if it needs cleaning or further analysisUnsupported
L1 Benchmark (s) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Rank has 2 (2.2%) zerosZeros

Reproduction

Analysis started2024-02-17 20:16:14.074601
Analysis finished2024-02-17 20:16:30.668432
Duration16.59 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Athlete
Text

UNIQUE 

Distinct91
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:30.815720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length12.89011
Min length8

Characters and Unicode

Total characters1173
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)100.0%

Sample

1st rowZeke Grove
2nd rowBartek Lipka
3rd rowConrad Winnertz
4th rowRuan Potgieter
5th rowWilliam Kearney
ValueCountFrequency (%)
kim 4
 
2.2%
luke 3
 
1.6%
jordan 3
 
1.6%
jack 2
 
1.1%
smith 2
 
1.1%
justin 2
 
1.1%
matt 2
 
1.1%
ruan 2
 
1.1%
jones 2
 
1.1%
anthony 2
 
1.1%
Other values (160) 161
87.0%
2024-02-17T15:16:31.139703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 111
 
9.5%
e 94
 
8.0%
94
 
8.0%
n 92
 
7.8%
o 76
 
6.5%
i 66
 
5.6%
r 63
 
5.4%
t 48
 
4.1%
s 47
 
4.0%
l 44
 
3.8%
Other values (41) 438
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 893
76.1%
Uppercase Letter 186
 
15.9%
Space Separator 94
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 111
12.4%
e 94
10.5%
n 92
10.3%
o 76
 
8.5%
i 66
 
7.4%
r 63
 
7.1%
t 48
 
5.4%
s 47
 
5.3%
l 44
 
4.9%
u 40
 
4.5%
Other values (18) 212
23.7%
Uppercase Letter
ValueCountFrequency (%)
M 19
 
10.2%
C 18
 
9.7%
J 15
 
8.1%
L 12
 
6.5%
T 11
 
5.9%
K 11
 
5.9%
H 10
 
5.4%
R 10
 
5.4%
S 10
 
5.4%
A 9
 
4.8%
Other values (12) 61
32.8%
Space Separator
ValueCountFrequency (%)
94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1079
92.0%
Common 94
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 111
 
10.3%
e 94
 
8.7%
n 92
 
8.5%
o 76
 
7.0%
i 66
 
6.1%
r 63
 
5.8%
t 48
 
4.4%
s 47
 
4.4%
l 44
 
4.1%
u 40
 
3.7%
Other values (40) 398
36.9%
Common
ValueCountFrequency (%)
94
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1171
99.8%
None 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 111
 
9.5%
e 94
 
8.0%
94
 
8.0%
n 92
 
7.9%
o 76
 
6.5%
i 66
 
5.6%
r 63
 
5.4%
t 48
 
4.1%
s 47
 
4.0%
l 44
 
3.8%
Other values (39) 436
37.2%
None
ValueCountFrequency (%)
ã 1
50.0%
á 1
50.0%

Affiliate
Text

MISSING 

Distinct73
Distinct (%)96.1%
Missing15
Missing (%)16.5%
Memory size1.4 KiB
2024-02-17T15:16:31.328199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length17.434211
Min length11

Characters and Unicode

Total characters1325
Distinct characters59
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)92.1%

Sample

1st rowCrossFit Carv
2nd rowCrossFit Strong House
3rd rowCrossFit Ground Zero
4th rowCrossFit Torian
5th rowSoyuz CrossFit
ValueCountFrequency (%)
crossfit 76
42.9%
solidarity 2
 
1.1%
limelight 2
 
1.1%
underway 2
 
1.1%
strong 2
 
1.1%
invictus 2
 
1.1%
tuluka 1
 
0.6%
finish 1
 
0.6%
zero 1
 
0.6%
ground 1
 
0.6%
Other values (87) 87
49.2%
2024-02-17T15:16:31.648898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 177
13.4%
o 121
 
9.1%
i 114
 
8.6%
r 112
 
8.5%
t 111
 
8.4%
101
 
7.6%
C 87
 
6.6%
F 79
 
6.0%
e 61
 
4.6%
a 53
 
4.0%
Other values (49) 309
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 948
71.5%
Uppercase Letter 259
 
19.5%
Space Separator 101
 
7.6%
Decimal Number 16
 
1.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 177
18.7%
o 121
12.8%
i 114
12.0%
r 112
11.8%
t 111
11.7%
e 61
 
6.4%
a 53
 
5.6%
n 33
 
3.5%
l 29
 
3.1%
d 22
 
2.3%
Other values (14) 115
12.1%
Uppercase Letter
ValueCountFrequency (%)
C 87
33.6%
F 79
30.5%
S 11
 
4.2%
L 8
 
3.1%
B 8
 
3.1%
P 6
 
2.3%
M 6
 
2.3%
N 6
 
2.3%
R 6
 
2.3%
E 5
 
1.9%
Other values (14) 37
14.3%
Decimal Number
ValueCountFrequency (%)
2 5
31.2%
6 2
 
12.5%
4 2
 
12.5%
0 2
 
12.5%
1 1
 
6.2%
7 1
 
6.2%
8 1
 
6.2%
5 1
 
6.2%
3 1
 
6.2%
Space Separator
ValueCountFrequency (%)
101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1207
91.1%
Common 118
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 177
14.7%
o 121
10.0%
i 114
9.4%
r 112
9.3%
t 111
9.2%
C 87
 
7.2%
F 79
 
6.5%
e 61
 
5.1%
a 53
 
4.4%
n 33
 
2.7%
Other values (38) 259
21.5%
Common
ValueCountFrequency (%)
101
85.6%
2 5
 
4.2%
6 2
 
1.7%
4 2
 
1.7%
0 2
 
1.7%
1 1
 
0.8%
7 1
 
0.8%
8 1
 
0.8%
5 1
 
0.8%
- 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1325
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 177
13.4%
o 121
 
9.1%
i 114
 
8.6%
r 112
 
8.5%
t 111
 
8.4%
101
 
7.6%
C 87
 
6.6%
F 79
 
6.0%
e 61
 
4.6%
a 53
 
4.0%
Other values (49) 309
23.3%

Country
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing91
Missing (%)100.0%
Memory size1.4 KiB

Region
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
worldwide
91 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters819
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 91
100.0%

Length

2024-02-17T15:16:31.790739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:16:31.871203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 91
100.0%

Most occurring characters

ValueCountFrequency (%)
w 182
22.2%
d 182
22.2%
o 91
11.1%
r 91
11.1%
l 91
11.1%
i 91
11.1%
e 91
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 819
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 182
22.2%
d 182
22.2%
o 91
11.1%
r 91
11.1%
l 91
11.1%
i 91
11.1%
e 91
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 819
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 182
22.2%
d 182
22.2%
o 91
11.1%
r 91
11.1%
l 91
11.1%
i 91
11.1%
e 91
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 182
22.2%
d 182
22.2%
o 91
11.1%
r 91
11.1%
l 91
11.1%
i 91
11.1%
e 91
11.1%

Division
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Men
91 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters273
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMen
2nd rowMen
3rd rowMen
4th rowMen
5th rowMen

Common Values

ValueCountFrequency (%)
Men 91
100.0%

Length

2024-02-17T15:16:31.953283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:16:32.030071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
men 91
100.0%

Most occurring characters

ValueCountFrequency (%)
M 91
33.3%
e 91
33.3%
n 91
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 182
66.7%
Uppercase Letter 91
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 91
50.0%
n 91
50.0%
Uppercase Letter
ValueCountFrequency (%)
M 91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 273
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 91
33.3%
e 91
33.3%
n 91
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 91
33.3%
e 91
33.3%
n 91
33.3%

Rank
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.912088
Minimum0
Maximum59
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:32.122642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.5
Q115
median25
Q337.5
95-th percentile55
Maximum59
Range59
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation16.135295
Coefficient of variation (CV)0.59955567
Kurtosis-0.8327726
Mean26.912088
Median Absolute Deviation (MAD)11
Skewness0.39407481
Sum2449
Variance260.34774
MonotonicityIncreasing
2024-02-17T15:16:32.252252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
29 5
 
5.5%
25 4
 
4.4%
28 4
 
4.4%
20 3
 
3.3%
50 3
 
3.3%
17 3
 
3.3%
16 3
 
3.3%
15 3
 
3.3%
9 3
 
3.3%
55 3
 
3.3%
Other values (39) 57
62.6%
ValueCountFrequency (%)
0 2
2.2%
2 1
 
1.1%
3 1
 
1.1%
4 1
 
1.1%
5 1
 
1.1%
6 2
2.2%
7 1
 
1.1%
8 2
2.2%
9 3
3.3%
10 2
2.2%
ValueCountFrequency (%)
59 1
 
1.1%
58 2
2.2%
56 1
 
1.1%
55 3
3.3%
54 1
 
1.1%
52 1
 
1.1%
51 2
2.2%
50 3
3.3%
49 2
2.2%
47 1
 
1.1%

Games_Level
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
worldwide
91 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters819
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 91
100.0%

Length

2024-02-17T15:16:32.367209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:16:32.444647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 91
100.0%

Most occurring characters

ValueCountFrequency (%)
w 182
22.2%
d 182
22.2%
o 91
11.1%
r 91
11.1%
l 91
11.1%
i 91
11.1%
e 91
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 819
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 182
22.2%
d 182
22.2%
o 91
11.1%
r 91
11.1%
l 91
11.1%
i 91
11.1%
e 91
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 819
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 182
22.2%
d 182
22.2%
o 91
11.1%
r 91
11.1%
l 91
11.1%
i 91
11.1%
e 91
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 182
22.2%
d 182
22.2%
o 91
11.1%
r 91
11.1%
l 91
11.1%
i 91
11.1%
e 91
11.1%

Qualifier
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
semifinals
91 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters910
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsemifinals
2nd rowsemifinals
3rd rowsemifinals
4th rowsemifinals
5th rowsemifinals

Common Values

ValueCountFrequency (%)
semifinals 91
100.0%

Length

2024-02-17T15:16:32.526869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:16:32.604174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
semifinals 91
100.0%

Most occurring characters

ValueCountFrequency (%)
s 182
20.0%
i 182
20.0%
e 91
10.0%
m 91
10.0%
f 91
10.0%
n 91
10.0%
a 91
10.0%
l 91
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 910
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 182
20.0%
i 182
20.0%
e 91
10.0%
m 91
10.0%
f 91
10.0%
n 91
10.0%
a 91
10.0%
l 91
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 910
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 182
20.0%
i 182
20.0%
e 91
10.0%
m 91
10.0%
f 91
10.0%
n 91
10.0%
a 91
10.0%
l 91
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 910
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 182
20.0%
i 182
20.0%
e 91
10.0%
m 91
10.0%
f 91
10.0%
n 91
10.0%
a 91
10.0%
l 91
10.0%

Back Squat (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)44.7%
Missing6
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean432.24248
Minimum264.5544
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:32.691775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum264.5544
5-th percentile374.82832
Q1405
median430
Q3455
95-th percentile500
Maximum600
Range335.4456
Interquartile range (IQR)50

Descriptive statistics

Standard deviation45.99563
Coefficient of variation (CV)0.10641164
Kurtosis3.9360402
Mean432.24248
Median Absolute Deviation (MAD)25
Skewness-0.077086624
Sum36740.611
Variance2115.598
MonotonicityNot monotonic
2024-02-17T15:16:32.813409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
440.924 8
 
8.8%
405 6
 
6.6%
418.8778 6
 
6.6%
396.8316 6
 
6.6%
455 5
 
5.5%
445 4
 
4.4%
407.8547 3
 
3.3%
425 3
 
3.3%
374.7854 3
 
3.3%
475 3
 
3.3%
Other values (28) 38
41.8%
(Missing) 6
 
6.6%
ValueCountFrequency (%)
264.5544 1
 
1.1%
285 1
 
1.1%
374.7854 3
3.3%
375 1
 
1.1%
385 1
 
1.1%
385.8085 2
 
2.2%
396.8316 6
6.6%
399.03622 1
 
1.1%
400 2
 
2.2%
405 6
6.6%
ValueCountFrequency (%)
600 1
 
1.1%
535 2
2.2%
505 1
 
1.1%
500 2
2.2%
490 1
 
1.1%
485.0164 1
 
1.1%
485 1
 
1.1%
475 3
3.3%
473.9933 1
 
1.1%
465 2
2.2%

Clean and Jerk (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)54.1%
Missing6
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean331.26854
Minimum160
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:32.938736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile277.462
Q1315
median335
Q3350
95-th percentile392.46528
Maximum410
Range250
Interquartile range (IQR)35

Descriptive statistics

Standard deviation37.458153
Coefficient of variation (CV)0.11307489
Kurtosis5.9581774
Mean331.26854
Median Absolute Deviation (MAD)17
Skewness-1.4873028
Sum28157.826
Variance1403.1132
MonotonicityNot monotonic
2024-02-17T15:16:33.067425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
341.7161 6
 
6.6%
330.693 5
 
5.5%
335 5
 
5.5%
319.6699 5
 
5.5%
305 4
 
4.4%
340 4
 
4.4%
355 4
 
4.4%
350 3
 
3.3%
352.7392 3
 
3.3%
325 3
 
3.3%
Other values (36) 43
47.3%
(Missing) 6
 
6.6%
ValueCountFrequency (%)
160 1
1.1%
198.4158 1
1.1%
253.5313 1
1.1%
273.37288 1
1.1%
275.5775 1
1.1%
285 1
1.1%
286.6006 1
1.1%
290 1
1.1%
295 1
1.1%
297.6237 1
1.1%
ValueCountFrequency (%)
410 1
1.1%
405 1
1.1%
400 2
2.2%
396.8316 1
1.1%
375 1
1.1%
374.7854 1
1.1%
372 1
1.1%
365 2
2.2%
360 1
1.1%
357.14844 1
1.1%

Deadlift (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)57.8%
Missing8
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean514.79971
Minimum308.6468
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:33.189208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum308.6468
5-th percentile445
Q1485.0164
median509.26722
Q3537.5
95-th percentile604.5
Maximum650
Range341.3532
Interquartile range (IQR)52.4836

Descriptive statistics

Standard deviation52.233348
Coefficient of variation (CV)0.10146344
Kurtosis2.3821424
Mean514.79971
Median Absolute Deviation (MAD)24.26722
Skewness-0.14312162
Sum42728.376
Variance2728.3226
MonotonicityNot monotonic
2024-02-17T15:16:33.321219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
500 6
 
6.6%
529.1088 5
 
5.5%
485.0164 5
 
5.5%
462.9702 5
 
5.5%
525 4
 
4.4%
507.0626 4
 
4.4%
600 4
 
4.4%
515 3
 
3.3%
485 3
 
3.3%
505 2
 
2.2%
Other values (38) 42
46.2%
(Missing) 8
 
8.8%
ValueCountFrequency (%)
308.6468 1
 
1.1%
440.924 2
 
2.2%
441 1
 
1.1%
445 2
 
2.2%
451.9471 1
 
1.1%
455 1
 
1.1%
462.9702 5
5.5%
465 1
 
1.1%
473.9933 1
 
1.1%
475 1
 
1.1%
ValueCountFrequency (%)
650 1
 
1.1%
635 1
 
1.1%
617.2936 1
 
1.1%
610 1
 
1.1%
605 1
 
1.1%
600 4
4.4%
585 1
 
1.1%
584.2243 1
 
1.1%
570 1
 
1.1%
565 1
 
1.1%

Snatch (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)44.7%
Missing6
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean265.93964
Minimum154.3234
Maximum342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:33.447152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum154.3234
5-th percentile223.1333
Q1255.73592
median265
Q3279.98674
95-th percentile301.6
Maximum342
Range187.6766
Interquartile range (IQR)24.25082

Descriptive statistics

Standard deviation27.470928
Coefficient of variation (CV)0.1032976
Kurtosis2.9789073
Mean265.93964
Median Absolute Deviation (MAD)14.98674
Skewness-0.80635836
Sum22604.869
Variance754.65187
MonotonicityNot monotonic
2024-02-17T15:16:33.578937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
264.5544 9
 
9.9%
265 8
 
8.8%
275.5775 7
 
7.7%
275 7
 
7.7%
295 4
 
4.4%
260 4
 
4.4%
250 3
 
3.3%
245 3
 
3.3%
300 3
 
3.3%
242.5082 2
 
2.2%
Other values (28) 35
38.5%
(Missing) 6
 
6.6%
ValueCountFrequency (%)
154.3234 1
1.1%
198.4158 1
1.1%
209.4389 1
1.1%
210 1
1.1%
222.66662 1
1.1%
225 2
2.2%
229 1
1.1%
231.4851 1
1.1%
235 1
1.1%
235.89434 1
1.1%
ValueCountFrequency (%)
342 1
 
1.1%
315 2
2.2%
308.6468 1
 
1.1%
302 1
 
1.1%
300 3
3.3%
297.6237 1
 
1.1%
295 4
4.4%
290 2
2.2%
286.6006 2
2.2%
285 2
2.2%

Fight Gone Bad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing72
Missing (%)79.1%
Infinite0
Infinite (%)0.0%
Mean412.05263
Minimum309
Maximum483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:33.688559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum309
5-th percentile313.5
Q1374
median418
Q3470.5
95-th percentile482.1
Maximum483
Range174
Interquartile range (IQR)96.5

Descriptive statistics

Standard deviation58.772134
Coefficient of variation (CV)0.14263259
Kurtosis-1.1916517
Mean412.05263
Median Absolute Deviation (MAD)49
Skewness-0.28742139
Sum7829
Variance3454.1637
MonotonicityNot monotonic
2024-02-17T15:16:33.792986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
337 1
 
1.1%
475 1
 
1.1%
448 1
 
1.1%
309 1
 
1.1%
314 1
 
1.1%
466 1
 
1.1%
481 1
 
1.1%
365 1
 
1.1%
477 1
 
1.1%
483 1
 
1.1%
Other values (9) 9
 
9.9%
(Missing) 72
79.1%
ValueCountFrequency (%)
309 1
1.1%
314 1
1.1%
337 1
1.1%
365 1
1.1%
369 1
1.1%
379 1
1.1%
380 1
1.1%
384 1
1.1%
387 1
1.1%
418 1
1.1%
ValueCountFrequency (%)
483 1
1.1%
482 1
1.1%
481 1
1.1%
477 1
1.1%
475 1
1.1%
466 1
1.1%
450 1
1.1%
448 1
1.1%
425 1
1.1%
418 1
1.1%

Max Pull-ups
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)63.0%
Missing45
Missing (%)49.5%
Infinite0
Infinite (%)0.0%
Mean62.086957
Minimum32
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:33.897900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile42
Q150
median60.5
Q374
95-th percentile82.75
Maximum90
Range58
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.249875
Coefficient of variation (CV)0.22951479
Kurtosis-0.6715282
Mean62.086957
Median Absolute Deviation (MAD)10.5
Skewness-0.060536502
Sum2856
Variance203.05894
MonotonicityNot monotonic
2024-02-17T15:16:34.003946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
50 8
 
8.8%
60 4
 
4.4%
79 2
 
2.2%
75 2
 
2.2%
32 2
 
2.2%
42 2
 
2.2%
74 2
 
2.2%
55 2
 
2.2%
82 2
 
2.2%
66 1
 
1.1%
Other values (19) 19
20.9%
(Missing) 45
49.5%
ValueCountFrequency (%)
32 2
 
2.2%
42 2
 
2.2%
45 1
 
1.1%
46 1
 
1.1%
50 8
8.8%
54 1
 
1.1%
55 2
 
2.2%
57 1
 
1.1%
59 1
 
1.1%
60 4
4.4%
ValueCountFrequency (%)
90 1
1.1%
85 1
1.1%
83 1
1.1%
82 2
2.2%
80 1
1.1%
79 2
2.2%
77 1
1.1%
75 2
2.2%
74 2
2.2%
73 1
1.1%

Chad1000x (s)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing91
Missing (%)100.0%
Memory size1.4 KiB

L1 Benchmark (s)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing91
Missing (%)100.0%
Memory size1.4 KiB

Filthy 50 (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing83
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean1251.125
Minimum922
Maximum1952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:34.107251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum922
5-th percentile964.7
Q11048.5
median1171.5
Q31275.5
95-th percentile1792.4
Maximum1952
Range1030
Interquartile range (IQR)227

Descriptive statistics

Standard deviation329.09809
Coefficient of variation (CV)0.26304174
Kurtosis2.7054119
Mean1251.125
Median Absolute Deviation (MAD)124.5
Skewness1.63059
Sum10009
Variance108305.55
MonotonicityNot monotonic
2024-02-17T15:16:34.204425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1050 1
 
1.1%
1162 1
 
1.1%
1496 1
 
1.1%
1952 1
 
1.1%
1044 1
 
1.1%
922 1
 
1.1%
1181 1
 
1.1%
1202 1
 
1.1%
(Missing) 83
91.2%
ValueCountFrequency (%)
922 1
1.1%
1044 1
1.1%
1050 1
1.1%
1162 1
1.1%
1181 1
1.1%
1202 1
1.1%
1496 1
1.1%
1952 1
1.1%
ValueCountFrequency (%)
1952 1
1.1%
1496 1
1.1%
1202 1
1.1%
1181 1
1.1%
1162 1
1.1%
1050 1
1.1%
1044 1
1.1%
922 1
1.1%

Fran (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)61.5%
Missing26
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean141.35385
Minimum110
Maximum252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:34.309298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile118.4
Q1126
median134
Q3149
95-th percentile179.2
Maximum252
Range142
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.659456
Coefficient of variation (CV)0.16030308
Kurtosis8.1856867
Mean141.35385
Median Absolute Deviation (MAD)10
Skewness2.2769089
Sum9188
Variance513.45096
MonotonicityNot monotonic
2024-02-17T15:16:34.424073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
133 5
 
5.5%
144 4
 
4.4%
124 4
 
4.4%
128 4
 
4.4%
149 3
 
3.3%
125 2
 
2.2%
118 2
 
2.2%
134 2
 
2.2%
153 2
 
2.2%
130 2
 
2.2%
Other values (30) 35
38.5%
(Missing) 26
28.6%
ValueCountFrequency (%)
110 1
 
1.1%
116 1
 
1.1%
118 2
2.2%
120 2
2.2%
121 1
 
1.1%
122 1
 
1.1%
123 1
 
1.1%
124 4
4.4%
125 2
2.2%
126 2
2.2%
ValueCountFrequency (%)
252 1
1.1%
199 1
1.1%
185 1
1.1%
180 1
1.1%
176 1
1.1%
175 1
1.1%
167 1
1.1%
163 1
1.1%
158 1
1.1%
157 1
1.1%

Grace (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)72.1%
Missing48
Missing (%)52.7%
Infinite0
Infinite (%)0.0%
Mean100.72093
Minimum64
Maximum217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:34.532419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile69
Q183.5
median96
Q3113
95-th percentile147.4
Maximum217
Range153
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation28.719767
Coefficient of variation (CV)0.285142
Kurtosis5.4280918
Mean100.72093
Median Absolute Deviation (MAD)15
Skewness1.8481066
Sum4331
Variance824.82503
MonotonicityNot monotonic
2024-02-17T15:16:34.653703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
69 3
 
3.3%
86 3
 
3.3%
90 3
 
3.3%
105 2
 
2.2%
102 2
 
2.2%
85 2
 
2.2%
113 2
 
2.2%
100 2
 
2.2%
121 2
 
2.2%
112 1
 
1.1%
Other values (21) 21
23.1%
(Missing) 48
52.7%
ValueCountFrequency (%)
64 1
 
1.1%
67 1
 
1.1%
69 3
3.3%
71 1
 
1.1%
76 1
 
1.1%
79 1
 
1.1%
81 1
 
1.1%
82 1
 
1.1%
83 1
 
1.1%
84 1
 
1.1%
ValueCountFrequency (%)
217 1
1.1%
162 1
1.1%
148 1
1.1%
142 1
1.1%
129 1
1.1%
121 2
2.2%
118 1
1.1%
117 1
1.1%
115 1
1.1%
113 2
2.2%

Helen (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)94.7%
Missing72
Missing (%)79.1%
Infinite0
Infinite (%)0.0%
Mean466.47368
Minimum419
Maximum585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:34.760832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum419
5-th percentile421.7
Q1436
median450
Q3487.5
95-th percentile548.1
Maximum585
Range166
Interquartile range (IQR)51.5

Descriptive statistics

Standard deviation44.079938
Coefficient of variation (CV)0.094496087
Kurtosis1.6261054
Mean466.47368
Median Absolute Deviation (MAD)27
Skewness1.3202884
Sum8863
Variance1943.0409
MonotonicityNot monotonic
2024-02-17T15:16:34.859385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
450 2
 
2.2%
433 1
 
1.1%
440 1
 
1.1%
477 1
 
1.1%
585 1
 
1.1%
434 1
 
1.1%
425 1
 
1.1%
490 1
 
1.1%
422 1
 
1.1%
544 1
 
1.1%
Other values (8) 8
 
8.8%
(Missing) 72
79.1%
ValueCountFrequency (%)
419 1
1.1%
422 1
1.1%
425 1
1.1%
433 1
1.1%
434 1
1.1%
438 1
1.1%
440 1
1.1%
442 1
1.1%
444 1
1.1%
450 2
2.2%
ValueCountFrequency (%)
585 1
1.1%
544 1
1.1%
501 1
1.1%
500 1
1.1%
490 1
1.1%
485 1
1.1%
484 1
1.1%
477 1
1.1%
450 2
2.2%
444 1
1.1%

Run 5k (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)94.4%
Missing55
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean1204.1944
Minimum1050
Maximum1570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:34.966517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1050
5-th percentile1075.25
Q11140
median1192
Q31264.5
95-th percentile1349.5
Maximum1570
Range520
Interquartile range (IQR)124.5

Descriptive statistics

Standard deviation102.32269
Coefficient of variation (CV)0.084971899
Kurtosis3.2536914
Mean1204.1944
Median Absolute Deviation (MAD)62
Skewness1.3273446
Sum43351
Variance10469.933
MonotonicityNot monotonic
2024-02-17T15:16:35.087470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1200 2
 
2.2%
1140 2
 
2.2%
1152 1
 
1.1%
1073 1
 
1.1%
1246 1
 
1.1%
1080 1
 
1.1%
1207 1
 
1.1%
1142 1
 
1.1%
1151 1
 
1.1%
1309 1
 
1.1%
Other values (24) 24
26.4%
(Missing) 55
60.4%
ValueCountFrequency (%)
1050 1
1.1%
1073 1
1.1%
1076 1
1.1%
1080 1
1.1%
1105 1
1.1%
1113 1
1.1%
1124 1
1.1%
1136 1
1.1%
1140 2
2.2%
1142 1
1.1%
ValueCountFrequency (%)
1570 1
1.1%
1363 1
1.1%
1345 1
1.1%
1309 1
1.1%
1308 1
1.1%
1300 1
1.1%
1293 1
1.1%
1290 1
1.1%
1272 1
1.1%
1262 1
1.1%

Sprint 400m (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)65.2%
Missing68
Missing (%)74.7%
Infinite0
Infinite (%)0.0%
Mean59.913043
Minimum49
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-02-17T15:16:35.185239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile52
Q155.5
median59
Q363
95-th percentile71.5
Maximum83
Range34
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation7.3541148
Coefficient of variation (CV)0.12274647
Kurtosis3.4073237
Mean59.913043
Median Absolute Deviation (MAD)4
Skewness1.4474978
Sum1378
Variance54.083004
MonotonicityNot monotonic
2024-02-17T15:16:35.285771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
57 3
 
3.3%
61 2
 
2.2%
58 2
 
2.2%
63 2
 
2.2%
52 2
 
2.2%
54 2
 
2.2%
59 2
 
2.2%
60 1
 
1.1%
53 1
 
1.1%
83 1
 
1.1%
Other values (5) 5
 
5.5%
(Missing) 68
74.7%
ValueCountFrequency (%)
49 1
 
1.1%
52 2
2.2%
53 1
 
1.1%
54 2
2.2%
57 3
3.3%
58 2
2.2%
59 2
2.2%
60 1
 
1.1%
61 2
2.2%
63 2
2.2%
ValueCountFrequency (%)
83 1
1.1%
72 1
1.1%
67 1
1.1%
65 1
1.1%
64 1
1.1%
63 2
2.2%
61 2
2.2%
60 1
1.1%
59 2
2.2%
58 2
2.2%

Interactions

2024-02-17T15:16:28.968012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:14.448927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.530324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.589317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.646378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.792708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.142507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.282107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.305791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.319768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:24.340345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.996015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.994842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.046220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:14.548773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.612680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.668144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.730432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.877527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.239532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.360804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.389088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.398409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:24.422254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.074035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.068686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.127158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:14.645511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.697944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.750892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.818016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.972314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.333932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.441951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.467622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.479411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.109681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.145506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.144504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.203293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:14.724149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.777598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.825430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.898266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:19.067583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.429758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.516512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.549320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.561056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.191192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.228136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.218051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.285419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:14.808077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.863875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.909573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.983814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:19.166866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.535658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.598569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.621383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.636121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.271150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.308984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.286510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.354207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:14.896355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.952676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.996831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.076651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:19.257702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.623042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.683593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.696710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.718579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.357938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.385708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.366003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.428087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:14.971700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.030629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.082369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.183048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:19.341452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.703002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.759680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.780574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.804979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.441669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.463860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.450874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.507715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.051547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.110812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.159502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.287040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:19.436997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.775716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.836807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.851175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.879324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.521236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.535736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.533799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.587365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.129604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.186798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.238195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.374746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:19.544698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.858893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.909328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.924116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.954360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.604219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.616368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.606682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.664168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.208957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.268228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.318060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.466945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:19.648517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.947753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.988035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.998453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:24.031809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.679758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.687568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.678828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.744207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.295326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.357343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.402915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.556969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:19.778986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.036966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.072886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.079846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:24.114455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.767027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.772208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.750312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.820508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.379389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.434362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.485738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.637954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:19.911646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.116283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.150185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.164176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:24.189542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.848672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.848776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.825353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:29.893907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:15.451633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:16.507675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:17.558002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:18.708232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:20.032207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:21.207361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:22.226645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:23.238732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:24.260017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:26.919376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:27.919475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:16:28.893018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-17T15:16:35.372711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Back Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Fight Gone BadFilthy 50 (s)Fran (s)Grace (s)Helen (s)Max Pull-upsRankRun 5k (s)Snatch (lbs)Sprint 400m (s)
Back Squat (lbs)1.0000.7430.5620.297-0.286-0.388-0.371-0.2560.2090.115-0.1220.565-0.445
Clean and Jerk (lbs)0.7431.0000.4960.4780.679-0.299-0.413-0.3940.1730.1700.0540.763-0.432
Deadlift (lbs)0.5620.4961.0000.299-0.288-0.265-0.304-0.5160.3350.172-0.1380.448-0.521
Fight Gone Bad0.2970.4780.2991.0000.800-0.373-0.507-0.2240.1300.1760.5760.5010.286
Filthy 50 (s)-0.2860.679-0.2880.8001.0000.108-0.429-0.5000.4060.0120.3930.9011.000
Fran (s)-0.388-0.299-0.265-0.3730.1081.0000.5380.382-0.2990.2330.147-0.1250.145
Grace (s)-0.371-0.413-0.304-0.507-0.4290.5381.0000.387-0.017-0.0480.295-0.3500.271
Helen (s)-0.256-0.394-0.516-0.224-0.5000.3820.3871.000-0.479-0.026-0.266-0.4330.209
Max Pull-ups0.2090.1730.3350.1300.406-0.299-0.017-0.4791.000-0.100-0.2920.307-0.370
Rank0.1150.1700.1720.1760.0120.233-0.048-0.026-0.1001.0000.0140.137-0.303
Run 5k (s)-0.1220.054-0.1380.5760.3930.1470.295-0.266-0.2920.0141.0000.3130.715
Snatch (lbs)0.5650.7630.4480.5010.901-0.125-0.350-0.4330.3070.1370.3131.000-0.411
Sprint 400m (s)-0.445-0.432-0.5210.2861.0000.1450.2710.209-0.370-0.3030.715-0.4111.000

Missing values

2024-02-17T15:16:30.033380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-17T15:16:30.299201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-17T15:16:30.516855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
57067Zeke GroveCrossFit CarvNaNworldwideMen0.0worldwidesemifinals440.92400348.32996518.08570275.57750NaN66.0NaNNaNNaN116.069.0433.0NaNNaN
57068Bartek LipkaCrossFit Strong HouseNaNworldwideMen0.0worldwidesemifinals490.00000340.00000635.00000260.00000NaN82.0NaNNaNNaN124.096.0NaN1140.057.0
57072Conrad WinnertzCrossFit Ground ZeroNaNworldwideMen2.0worldwidesemifinalsNaNNaNNaNNaNNaNNaNNaNNaNNaN175.0NaNNaNNaN60.0
57074Ruan PotgieterNaNNaNworldwideMen3.0worldwidesemifinals418.87780330.69300507.06260264.55440337.079.0NaNNaNNaN134.0NaNNaNNaNNaN
57075William KearneyCrossFit TorianNaNworldwideMen4.0worldwidesemifinals429.90090335.10224500.44874264.55440380.0NaNNaNNaNNaNNaN121.0501.01178.061.0
57077Andrei FedotovSoyuz CrossFitNaNworldwideMen5.0worldwidesemifinals451.94710341.71610529.10880275.57750NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
57078Agustin RichelmeNaNNaNworldwideMen6.0worldwidesemifinals429.90090352.73920485.01640275.57750NaN79.0NaNNaNNaN118.0NaNNaNNaNNaN
57079Reilly SmithCrossFit NewsteadNaNworldwideMen6.0worldwidesemifinals407.85470319.66990462.97020249.12206NaNNaNNaNNaNNaN152.0118.0NaNNaNNaN
57081Gustavo ErricoNaNNaNworldwideMen7.0worldwidesemifinals405.00000305.00000445.00000250.00000NaN75.0NaNNaNNaN133.0100.0NaNNaNNaN
57083Justin Holliday24N46E CrossFitNaNworldwideMen8.0worldwidesemifinals399.03622295.00000462.97020229.00000NaN32.0NaNNaNNaNNaN129.0NaNNaNNaN
AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
57167Luke BurnsSolidarity CrossFitNaNworldwideMen51.0worldwidesemifinals405.0000315.00000500.0000250.00000NaN63.0NaNNaNNaN135.0NaN477.01073.0NaN
57168Harley PescosolidoDriven to Conquer CrossFitNaNworldwideMen52.0worldwidesemifinals400.0000336.00000610.0000265.00000NaNNaNNaNNaNNaN252.0NaNNaNNaNNaN
57169Zane PariseBranford CrossFitNaNworldwideMen54.0worldwidesemifinals385.0000290.00000445.0000225.00000NaNNaNNaNNaNNaNNaNNaNNaNNaN63.0
57170Daniel CamachoCrossFit ZarautzNaNworldwideMen55.0worldwidesemifinals396.8316330.69300485.0164286.60060NaN60.0NaNNaNNaN158.0NaNNaNNaN57.0
57171Alessandro ZanetK2 CrossFitNaNworldwideMen55.0worldwidesemifinals425.0000325.00000500.0000275.00000NaN80.0NaNNaNNaN133.090.0NaNNaNNaN
57172Connor DuddySolidarity CrossFitNaNworldwideMen55.0worldwidesemifinals430.0000335.00000500.0000280.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
57174Saxon PanchikCrossFit East NashvilleNaNworldwideMen56.0worldwidesemifinals475.0000360.00000535.0000295.00000NaN62.0NaNNaNNaN121.069.0438.01152.052.0
57175Giorgos KaravisNaNNaNworldwideMen58.0worldwidesemifinals485.0164354.94382529.1088279.98674NaN50.0NaNNaNNaNNaNNaNNaNNaNNaN
57176Alexandre CaronCrossFit LevisNaNworldwideMen58.0worldwidesemifinals460.0000355.00000555.0000275.00000NaN50.0NaNNaN1202.0143.090.0NaN1142.0NaN
57177Matthew GreeneCrossFit WildwoodNaNworldwideMen59.0worldwidesemifinals420.0000340.00000515.0000275.00000NaN61.0NaNNaNNaN136.0105.0450.01076.059.0